A machine learning-based predictive model for stem cell therapy outcomes in plastic surgery

Early human evidence such as case series or small samples is exploring possible benefits.

• Level A · Stronger Clinical Evidence
• Level B · Emerging clinical evidence with positive signals
• Level C · Early human research exploring benefits
• Level D · Scientific groundwork from lab and animal studies
• Emerging · Emerging topic under active research

Abstract (original English)

Objective Stem cell therapy has emerged as a promising approach in plastic surgery, yet its efficacy varies markedly among individuals and lacks reliable predictive assessment tools. This study aimed to construct and validate a predictive model for assessing the therapeutic efficacy of stem cell therapy in plastic surgery by identifying key influencing factors through clinical data analysis and machine learning. Methods Patients who underwent stem cell therapy in the Department of Plastic Surgery from June 2021 to July 2024 were retrospectively included and randomly divided into a training set and a validation set at a 7:3 ratio. Baseline clinical data were collected, and independent influencing factors were screened via univariate analysis, followed by multivariate logistic regression and LASSO feature selection in the training set. Three machine learning models-random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN)-were constructed using Python 3.8.5 and the scikit-learn library, followed by performance validation in the validation set. Results A total of 620 patients who underwent stem cell therapy were included. In the training set ( n = 434), 262 cases (60.37%) showed effective treatment outcomes, while 112 cases (60.23%) were effective in the validation set ( n = 186). Multivariate logistic regression revealed that age, disease duration, diabetes

Evidence level

Early human evidence such as case series or small samples is exploring possible benefits.

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